• Journal of the Korean GEO-environmental Society
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• v.16 no.2
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• pp.5-13
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• 2015

In the present study, the prediction methods of the crest settlement after impounding and the maximum internal settlement during dam construction were proposed through the analysis on settlement data at 38 monitored points of 36 Concrete-Faced Rockfill Dams (CFRDs). The results from this analysis provided that the crest settlement and the maximum internal settlement are increased in proportion to the dam height and the void ratio. However, the relationship between internal settlement and dam height for each void-ratio range plotted in semi-logarithmic scale is the nearly same. Also, the prediction of the crest settlement of the CFRD is possible through the maximum internal settlement during dam construction. In addition, it seems that the valley shape highly affects the dense dam body with high construction modulus. The results of this study will provide the useful tool for the design, construction and management of CFRDs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.535-541
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• 2011

A debris flow is caused by torrential rain in mountainous regions and carries mixture of fragmental matter from slope failure, deposit soils from a valley floor and a large amount of water. It seriously damages facilities, houses, and human lives in its path. We tried to apply debris flow behavior estimation model developed in foreign country to domestic case. The study area is Inje-county, Gangwon-do and aerial photos and GPS surveying were used to collect information of starting and end point of the landslide and debris flow. The analysis showed that L/H for forecasting the travel distances of debris flows has the mean of 4.93 and standard deviation of 0.98. This model tended to overestimate the scale and extent of debris flows. In Inje-county's case, a debris flow is caused by multiple simultaneous small-scale landslide. This is quite different from the foreign cases in which a large-scale landslide cause a large-scale debris flow. Thus, an empirical model suitable for domestic conditions needs to be developed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of KIISE:Software and Applications
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• v.34 no.8
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• pp.752-762
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• 2007

The PSP (Personal Software Process) is developed to help developers make high-quality products through improving their personal process. With consistent measurement and analysis activity that the PSP suggests, developers can identify process deficiencies and make reliable estimates on effort and quality. However, due to the high-overhead and context-switching problem of manual data recording, developers have difficulties in collecting reliable data, which can lead wrong analysis results. On the other hand, the paper-based process guides of the PSP are inconvenient to navigate its process information and difficult to attach additional information. In this paper, we introduce a PSP supporting tool developed to handle these problems. The tool provides automated data collection facilities to help acquire reliable data, an EPG (Electronic Process Guide) for the PSP to provide easy access and navigation of the process information, and an experience repository to store development experience as additional information about the process.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.5939-5944
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• 2014

The foam monitor is equipment for extinguishing fires, particularly for oil tankers or cargo areas of the carrying vessel. This equipment is installed on the cargo tank deck. Generally, the projection distance is important for designing an extinguishment. On the other hand, the form monitors in current industry have been designed by trial and error rather than by numerical analysis method. Therefore, the shape design of the new form of monitor is needed. In this study, numerical analysis was performed to determine the projection distance prediction, and experiment results were used to make a comparison with the analysis results. The proposed method was applied to the modified form of a newly designed monitor in a company.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Journal of the Korean Institute of Gas
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• v.20 no.1
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• pp.13-22
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• 2016

This paper presents development of an appropriate procedure and flow chart to analyze shale gas production data obtained from a multi-fractured horizontal well according to flow characteristics in order to calculate an estimated ultimate recovery. Also, the technical considerations were proposed when a rate transient analysis was performed with field production data occurred to only $1^{st}$ transient flow. If production data show the $1^{st}$ transient flow from log-log and square root time plot analysis, production forecasting must be performed by applying different method as before and after of the end of $1^{st}$ linear flow. It is estimated by an area of stimulated reservoir volume which can be calculated from analysis results of micro-seismic data. If there are no bottomhole pressure data or micro-seismic data, an empirical decline curve method can be used to forecast production performance. If production period is relatively short, an accuracy of production data analysis could be improved by analyzing except the early production data, if it is necessary, after evaluating appropriation with near well data. Also, because over- or under-estimation for stimulated reservoir volume could take place according to analysis method or analyzer's own mind, it is necessary to recalculate it with fracture modeling, reservoir simulation and rate transient analysis, if it is necessary, after adequacy evaluation for fracture stage, injection volume of fracture fluid and productivity of producers.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1821-1841
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• 1997

This paper presents an automated protocol conformance test sequence generation based on formal methods for LOTOS specification by using and applying many existing related algorithms and technique, such as the testing framework, Rural Chinese Postman tour concepts. We use the state-transition graphs obtained from LOTOS specifications by means of the CAESAR tool. This tool compiles a specification written in LOTOS into an extended Petri net, from which a transition graph of a event finite-state machine(EvFSM) including data is generated. A new characterizing sequence(CS), called Unique Event sequence(UE sequence) is defined. An UE sequence for a state is a sequence of accepted gate events that is unique for this state. Some experiences about UE sequence, partial UE sequence and signature are also explained. These sequences are combined with the concept of the Rural Chinese Postman Tour to obtain an optimal test sequence which is a minimum cost tour of the reference transition graph of the EvFSM. This paper also presents a fault coverage estimation experience of an automated method for optimized test sequences generation and the translation of the test sequence obtained by using our tool to TTCN notation are also given. A prototype of the proposed framework has been built with special attention to real application in order to generated the executable test cases in an automatic way. This formal method on conformance testing can be applied to the protocols related to IN, PCS and ATM for the purpose of verifying the correctness of implementation with respect to the given specification.